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Twin studies measure the contribution of genetics (as opposed to environment) to a given trait or condition of interest. The classic twin study design relies on studying twins raised in the same family environments.

Monozygotic (identical) twins share all of their genes, while dizygotic (fraternal) twins share only about 50 percent of them, the same as non-twin siblings. If a researcher compares the similarity between sets of identical twins to that of fraternal twins for a particular trait, then any excess likeness between the identical twins should be due to genes rather than environment.

Study Population

Twins. Depending on the question you may need monozygotic or dizygotic twins, or a combination of the two.

Use of Twin Studies

Quantify the effect of a person's shared environment (family) and unique environment (the individual events that shape a life) on a trait

Variations of Twin Studies:

Twins-reared-apart design focuses on twins who were adopted into separate homes when they were infants. These twins do not share their environments, so genetic influences are thought to account for their similarities.

Longitudinal studies of twins follow twins over time to assess the development of adult-onset conditions and traits. This approach allows for a more complete and accurate assessment of environmental factors over time.

Combining classic twin studies with molecular genetics research incorporates data about the presence or absence of specific genetic variants to determine the impact on the trait of interest.

Study Design

Assumptions in the design

Random mating. Research on twins assumes that people are as likely to choose partners who are different from themselves as they are to choose partners who are similar for a particular trait. If, instead, people tend to choose mates like themselves, then fraternal twins could share a greater percentage of their genes than expected. In the case of non-random mating, fraternal twins would have more genetically influenced traits in common than expected, because the genes they receive from their mothers and fathers would be similar to each other.

Equal environments. Research on twins also assumes that fraternal and identical twins raised in the same homes experience equally similar environments. However, some research suggests that parents, teachers, peers, and others may treat identical twins more similarly than fraternal twins.

Gene-environment interaction. Twin studies typically assume that genes and environment have only separate and distinct contributions to a trait. However, for some research outcomes, interactions between genes and environment appear to influence the development and intensity of traits. This suggests that both the gene variant and the specific environmental factor must be present for the trait to occur. Many twin-study designs fail to take this possibility into account.

Genetic mechanisms. Twin studies, in general, assume that only one type of genetic mechanism--usually additive--is operating for a particular trait. However, traits can be inherited through different genetic mechanisms:

Additive genetic mechanisms mix together the effects of each allele. For example, if genes for height were additive, a very tall father and a very short mother might have a child who is of average height.

For traits governed by dominant genetic mechanisms, a dominant gene inherited from one parent trumps a recessive gene inherited from the other parent. For example, if a person inherits a recessive gene for blue eyes from one parent and a dominant gene for brown eyes from the other parent, then the dominant brown gene "wins", and the person's eyes are brown.

Epistatic mechanisms are complex cases where interactions among multiple genes may determine the outcome of one trait.

Note: Traditional concepts of dominant and recessive traits and diseases are being challenged as new evidence is gathered about subtle variations in phenotype and the complex etiology of common conditions. Some carriers of some recessive conditions have been found to show mild symptoms of the condition.

Advantages of Twin Studies

Allows disentanglement of the shared genetic and environmental factors for the trait of interest. Researchers can estimate the proportion of variance in a trait attributable to genetic variation, versus the proportion that is due to shared environment or unshared environment.

Limitations of Twin Studies

The use of twins does not allow the researcher to consider the effects of both shared environment and gene-environment interaction simultaneously. This can be addressed by including additional siblings in the design.

Results from twin studies cannot be directly generalized to the general population. Twins are not a random sample of the general population as they differ in their developmental environment because two fetuses are growing simultaneously. Genetic factors may lead to a higher incidence of twinning (e.g., women more likely to release two eggs during ovulation).

Hints for use

Research on mate selection patterns may help guide whether non-random mating can be assumed for a particular trait. For example, some studies suggest that people are more likely to select mates with similar levels of intelligence than they are to select mates with similar personality traits (i.e., introversion). Therefore, non-random mating may be more of a concern for researchers studying intelligence than those studying introversion.

Many of the assumptions of twin studies can be tested if appropriate data is available. For example, the effects of non-random mating may be investigated by including spouses of twins in studies.

Sequencing is a laboratory technique that identifies the unique and specific order of bases (A, C, G and T) in an individual's DNA. The sequence can be compared to other people's DNA to locate general areas of variation.

Sequencing may be performed on a region of DNA that has already been associated with a trait in other studies. The lab or researcher then determines which identified sequence differences are likely to be causative mutations by using information about the type of genetic change (e.g. missense or termination mutation) and the frequency of the variation within the general population.

Study Population

Sequencing is typically performed in individuals who have the trait or condition of interest.

Use of Sequencing

Researchers use this method, and variations on it to:

To determine whether a mutation(s) is present in a specific segment of DNA.

To refine data from other types of studies that rely on testing for the presence or absence of specific single nucleotide polymorphisms (SNPs) found throughout the genome.

For example, results from a genome wide association study (GWAS) may find an association between a trait and a particular SNP. The SNP may or may not be the causative mutation, and may or may not be within the causative gene. Sequencing the area containing the SNP will provide more detailed information about the sequence and allow for more informative comparison.

Variations of Sequencing

Sequencing technologies continue to change, becoming more rapid and accurate. The variations in the method depend on the research question being asked and the specific technique used. The research question will define the population and the genetic sequence(s) of interest. The technique will determine how many regions of the gene can be studied in a given time frame. Variations of sequencing include:

Exome sequencing

This method uses sequencing technology to find variations in the exons (protein-coding regions of the genome). It assumes that changes in the exons change the function of a protein, and are therefore more likely to lead to abnormal outcomes. Exome sequencing is used in both research and, in selected cases, clinically.

Whole genome sequencing

Using the same sequencing technology as in whole exome sequencing, whole genome sequencing is used to find changes in the entire genome, including the exons and introns (regions that do not code directly for genes but often help control gene expression and other functions). Whole genome sequencing is currently being used most often in a research setting, but it is now possible for individuals and providers to order this test directly. There are copious amounts of data produced by this method and the interpretation of those data remains a major challenge.

Study Design

Assumptions

The method of sequencing assumes that causative mutations are found in areas of the genome that can be sequenced. However, some areas are difficult to sequence, though improved technologies have made the entire genome more accessible.

Advantages of Sequencing

Considered the “gold standard” for detecting unknown gene mutations. It detects the greatest proportion of mutations in a gene.

Can be performed for any research question, but may not be necessary if a specific gene marker is already identified.

The candidate gene approach allows researchers to investigate the validity of an "educated guess" about the genetic basis of a trait. Candidate gene studies focus on genes that are selected because of a priori hypotheses about their etiological role in disease, often associated with an understanding of the underlying biological pathway. For example, research on risk-taking behavior might focus on genes associated with the development and function of neurotransmitters.

How are candidate genes identified?

Researchers test one or more variants within the candidate gene. The variants are selected based on the likelihood that they would produce a protein with altered function. The identification of single nucleotide polymorphisms (SNPs) within a gene can also be used to narrow the region of investigation. The variants used in candidate-gene studies may directly cause the problem or they may be nearby, in linkage disequilibrium, with the gene that actually causes the problem.

Study Population

Population-based sample of cases and controls (affected and unaffected individuals).

Use of Candidate Gene Studies

Test whether a common genetic mechanism is associated with two or more traits that are believed to have similar biological pathways (e.g., major depression and schizophrenia)

Variations of Candidate Gene Studies

Multiple family groupings (e.g., parents and child, siblings) may be used to increase the likelihood of finding an association. Using relatives takes advantage of the fact that a certain percentage of genes are shared between parents and children.

Study Design

Assumptions

Candidate gene studies assume that causative gene variants for a particular trait are within, or in linkage disequilibrium with, the candidate gene.

This approach also assumes that phenotype assessment of cases and controls was thorough and accurate.

Advantages of Candidate Gene Studies

Because the gene being tested is believed to have a role in the biological pathway of the trait, finding an association provides information about its functionality in that pathway. Similarly, failure to find an association with the trait of interest may provide evidence against the variant's function in that pathway.

This method takes advantage of the increased statistical efficiency of association analysis for complex diseases, and the understanding of the biological pathways that are likely to be involved in the disease.

Limitations of Candidate Gene Studies

To identify a candidate gene, the biology underlying the development of the trait has to be at least partially understood.

Because most traits are influenced by many different genes, each contributing a small amount to the overall risk, identifying the independent contribution of a single genetic variant in the pathway may be difficult unless the study population is very large.

Choosing the specific candidate gene may be somewhat subjective based on the understanding of the pathway and researcher's specific interest.

Because the search for variants is limited to a specific region of investigation, a study's ability to detect an association depends upon the accuracy of the researcher's assumptions about the function of a gene. The influence of causative variants outside this region will be missed.

Hints For Use

Prioritizing potential genes: Candidate gene studies may identify many potential genes, and an important step prior to the study is to prioritize the candidate genes. This involves considering the likelihood of a functional consequence to the biological pathway.

Look for issues of non-replication: Because findings in association studies are often not replicated in subsequent, independent studies, there is concern that the candidate-gene approach is unreliable. Reasons for non-replication include:

Variations in study design. Discrepant findings are often due to variations in study design. For example, subsequent studies may differ in the choice of study population and in the definition of the phenotype. The same candidate gene or DNA variants might be associated with different relative risks in different populations, and the non-replication might result from real biological differences.

Confounding and misclassification. "Complex disease" inherently involves multiple biological pathways. Depending on the definition of the phenotype measured, there is a strong likelihood of including individuals whose trait is caused by changes in more than one pathway. The broader the definition, the greater the likelihood. Confounding and misclassification are more likely to obscure small-to-moderate relative risks than larger relative risks.

Over-reliance on linkage disequilibrium. In some cases, it may be easier to detect a DNA marker that is nearby a gene of interest than it is to detect the actual gene. For this method to be effective, scientists must pick a marker that seems to travel along with the gene as it is passed from one generation to the next; an observation called linkage disequilibrium. Typically, the physical distance between two genes indicates how likely they are to be separated in the shuffling of genetic material that occurs during inheritance. However, assuming that a marker and a gene are in linkage disequilibrium may limit the ability to detect an association.

Lack of evidence. Initiation of a candidate gene study without supporting evidence from other studies (e.g., GWAS or linkage analysis) may limit the ability to detect association. Also, including candidate genes as part of a larger epidemiological study not originally designed to examine genetic factors may not allow for enough power to detect an association.

A basic understanding of the technology used for genetic testing can help illuminate the scope of information that various tests can provide, and their inherent limitations. In a clinical setting, multiple methods may be necessary to completely characterize the genetic variant that is contributing to a trait or condition.

How it's done

Direct gene testing can be performed when scientists know exactly where a gene is located on a chromosome and what changes in a gene causes a disease. In a specific gene or region of DNA, the sequence of nucleotide "letters" (A, T, C and G) is "read" to determine if any letters are missing, added, or exchanged. Alternatively, probes may be designed to attach to a section of a gene only if a particular mutation is present.

Limitations

Scientists must know exactly where a gene is located and what changes cause disease.

May miss larger structural changes, such as deletions or rearrangements.

Can be costly and time consuming.

Rare or unique family variants may not be detected by specific mutation panels.

Examples

Mutation analysis for cystic fibrosis is an example of direct DNA testing. Most screening panels look for the 25 most common CF mutations.

Karyotype

Purpose

A karyotype is used to detect large segments of missing, extra, or rearranged genetic material. It can be used as a diagnostic test for an adult, child, or fetus exhibiting structural or developmental difference. Karyotype is also used to identify parents who carry chromosome rearrangements that can result in offspring with abnormalities.

How it's done

Chromosomes are extracted from the nucleus of a single cell and observed under a microscope. Stains can be applied to reveal specific regions of DNA. Several cells may be observed for comparison.

Limitations

Some chromosome rearrangements are unique and have unclear clinical implications.

Examples

Down syndrome (Trisomy 21) and Turner syndrome (Monosomy X)

46 XY Normal Male 47 XX + 21,

Female with Trisomy 21 (Down syndrome)

Array-based Testing

Purpose

Testing multiple areas of the genome simultaneously, from a single sample. It can be used for diagnostic testing, carrier testing, and SNP identification in susceptibility testing.

How it's done

Segments of DNA that are of interest are attached to a slide. Fluorescently labeled DNA from both the patient and a known normal human control are applied to the slide and attach (or "hybridize") to their corresponding DNA segments. Computer software analyzes the fluorescent signals for segments where the patient and control DNA did not adhere at the same level. These differences suggest that the patient has a deletion or duplication in that area of the gene or genome.

Limitations

The analysis reveals the presence or absence of specific segments of DNA, but does not necessarily provide the sequence at that region.

Complex chromosomal rearrangements may be missed.

Rare or unique family mutations may be missed.

Examples

Chromosomal microarrays that look for small deletions and duplications across the entire genome is considered a first line test for children with developmental delay and/or autism.

Fluorescence in situ hybridization (FISH) Testing

Purpose

FISH is used to detect chromosome rearrangements, marker chromosomes (extra pieces of unidentified chromosomal material), and duplications or deletions of large pieces of DNA. It is typically used to enhance interpretation of karyotype or array testing. In some cases, FISH can be used to detect specific gene mutations or chromosomal regions associated with particular conditions.

How it's done

FISH uses a protein, called a probe, to “stick” to a known sequence of DNA-usually a known mutation. If that sequence is present in a patient’s sample, the probe will bind to it and light up under a fluorescent microscope.

Limitations

Cannot be used unless scientists know exactly where a variant is located and what changes cause disease.

May miss larger structural rearrangements if not performed in tandem with other tests.

Rare or unique family mutations may not be detected.

Will miss sequence changes in other areas.

Examples

Biochemical Testing

Purpose

Biochemical testing indicates the downstream effects of genetic changes in the proteins and enzymes that are encoded by genes. It is usually used for diagnostic testing when a genetic test is unavailable or less practical. Biochemical testing may be a type of screening used in a testing cascade, before proceeding to more costly, confirmatory genetic analysis. In some cases, biochemical testing can detect carriers as well. Biochemical testing is frequently used in newborn screening.

How it's done

Biochemical testing detects the presence or (absence) of proteins or enzymes, their quantity (extra, sufficient, reduced, absent), or their structure (normal vs. abnormal).

Limitations

Does not usually provide information about specific underlying DNA sequence changes.

Cannot distinguish between various genetic contributions to disease when more than one gene is associated with a phenotype.

Biochemical abnormalities may be caused by other non-genetic factors, be transient, or only detectable under certain metabolic circumstances.

Examples

Many inherited metabolic disorders (e.g., phenylketouria or PKU) are diagnosed through biochemical tests.

Linkage Testing

Purpose

Linkage testing is a type of indirect DNA testing used to narrow in on the location of a disease-causing gene within a family. It is typically used when the gene for a condition is undiscovered or when a family is thought to have a rare or unique mutation. Linkage can be used for diagnostic, carrier, and prenatal testing.

How it's done

Indirect testing requires blood from several family members, including those that are known to be carriers or affected with a specific condition. Because DNA contains markers that are unique to individuals and families, laboratories can find the markers unique to a family with a genetic disease. Those markers can then be traced through the family to identify carriers and/or to diagnose other affected individuals.

Within the last decade, a number of private companies have begun marketing and providing genetic tests directly to the consumer, usually via the internet. These companies vary widely in their laboratory, analytic, marketing, and service models, as well as in their involvement of a health care provider in the testing process. The direct to consumer (DTC) tests cover a spectrum from traditional diagnostic medical testing to personal genomic screening (PGS).

PGS is a type of DTC test that provides information about genetic risk for health conditions, drug responses, ancestry, and other traits (e.g., hair color, baldness, earwax type, muscle performance). This personal genomic screening is based primarily on results from genome wide association studies (GWAS), which use markers (SNPs) throughout the entire genome (rather than in a specific gene). These small variants are common in the general populations and may or may not have known functional consequences.

The results of GWAS may provide evidence for an association between a genetic variant and a trait. However, this approach does not allow for assessment of causality. This is in contrast to clinical genetic testing for classically inherited (Mendelian) traits. Classical testing has been validated clinically and analytically, has a specific clinical utility, and typically must be obtained through a health care provider.

PGS results typically indicate very small increments of increased or decreased risk for a trait. For example, the presence of a set of particular SNPs might indicate a slight (less than 1%) increase in lifetime risk for breast cancer. In contrast, traditional clinical genetic testing for the relatively well characterized BRCA1 gene might provide a lifetime risk assessment of 50-80% for breast cancer.

What are the Benefits and Concerns about DTC/PGS Testing?

At the time of this writing, there is no direct evidence that DTC and PGS benefit or harm the consumer. Increased access to genetic information is the main benefit cited by proponents of DTC genetic testing. One hope within the research and health professional communities has been that personalizing risk with genetic information will promote behavior change. The evidence for such an effect is scant, although a number of ongoing research projects examining that possibility.

Health professionals, ethicists, researchers, and others have raised concerns about the DTC approach to PGS. Concerns typically fall into three major, interrelated categories:

1. Are claims made by DTC companies valid?

Questions have been raised about the data used by DTC companies, their methods of risk calculation, the clinical utility of the tests, and the sale of "personalized" products such as supplements and skin creams based on genotyping.

2. What is the impact of PGS results on the consumer?

There are many unknowns about whether and how PGS results affect consumer psychosocial parameters, medical decision-making, behavior change, and utilization of health care. Primary concerns are that PGS will result in unnecessary anxiety, wasted money, and even harmful medical intervention.

3. How are PGS results integrated into medical management?

Some patient advocates and public health experts are concerned not only that health care providers are not being adequately consulted about PGS results, but also that providers do not have the knowledge base to appropriately interpret and use PGS results.

Genome-wide association studies (GWAS) use microarray technology to identify associations between specific outcomes (ie. height, aggression, or diabetes) and genetic variants across the entire genome, rather than in a specific gene. This method identifies markers or small variants that are common in the general population and may or may not have known functional consequences.

How are associations made?

A positive association arises when there is a greater frequency in the presence of a genetic variant is in individuals with a disease or trait than in unaffected individuals. An association identifies a genomic region, not a specific causative mutation that may be involved in the development of the disease or trait.

The human genome contains millions of single nucleotide polymorphisms (SNPs), which are single base pair changes that occur throughout the genome and are present in at least 1% of the population. The genome also contains thousands of variations in the number of copies of segments of a genome (copy number variants or CNVs). It is not efficient to read through all 6 billion nucleotides of each individual to identify differences. GWAS takes advantage of the fact that segments of the genome (haplotypes) are always inherited together. By looking at one nucleotide within a haplotype, the researcher knows the make-up of the entire segment. This understanding of genomic structure allows researchers to look at a relatively fewer SNPs to identify differences throughout the genome.

Statistical significance criteria

To achieve statistical significance, a GWAS must include at least 100,000 markers, most of which are inherited SNPs or copy number variants (CNVs) The large numbers of statistical tests that are necessary greatly increase the likelihood of false positives. Because of this risk, the accepted threshold for statistical significance in GWAS is <5*10-8.

Results from GWAS

A prominent use for GWAS has been to identify genetic variants associated with common disease or complex traits. For these classes of traits, both genetic and environmental factors have a prominent role, and it is considered likely that multiple genes contribute to the genetic portion of disease risk. Note that the identification of associated variants does not necessarily provide insights into the underlying biology of the trait in question.

Effect size

The effect sizes of associated SNPs are typically small, with odds ratios in the range of approximately 1.1 to 1.5. There is variation in effect size depending on:

The condition of interest,

The population selection,

The choice of SNP panel, and

The level of stringency in the definition of phenotype.

Study Population

A GWAS is a variation of a cross-sectional case-control study. Some research questions may require a random sample of the general population-for example, an investigation of genetic contributions to height. However, because genetic variants often have markedly different frequencies across populations, some associations can be best detected in homogeneous samples, such as those limited to a single ancestry group (ie. Ashkenazi Jewish or Japanese).

Use of GWAS

Researchers use this method, and variations on it, to:

Identify novel candidate genes, which is especially important in traits for which the biological pathway is unknown (e.g., psychiatric disorders).

Identify genetic contributors to common, complex disease for which each gene may contribute only a small increase in risk.

Variations of GWAS

The variations to GWAS involve using the same approach in different types of populations:

Affected sibling pair. This variation is used to increase the likelihood of finding pathological variants, compared to studies that use a random selection of individuals from the general population. The method relies on the fact that full siblings share ~50% of their genes. By using pairs of siblings in which one sib is affected, fewer genetic variations are likely to be found, and those that are have a greater chance of being causative.

Homozygosity mapping. The genomes of children whose parents share ancestors have more common regions than do other children. Therefore, there is a greater likelihood that they will inherit two copies of a causative mutation.

Study Design

Assumptions

GWAS studies assume that

genetic variants contribute to disease development in all complex diseases.

a SNP that is found in a population with a certain trait is in or near a gene that contributes to that trait.

common variants, as opposed to rare variants (those in <1% of the population) explain a significant proportion of the genetic variation seen in the population.

Advantages of GWAS

Biological pathway of the trait does not have to be known

Potential to discover novel candidate genes not identified through other methodological approaches

Encourages the formation of collaborative consortia to recruit sufficient numbers of participants for analysis, which tend to continue their collaboration for subsequent analyses

Rules out specific genetic associations

Provides data on the ancestry of each subject, which assists in matching case subjects with control subjects

Provides data on two types of structural variants--sequence and copy-number variations-- which provides more robust data.

Limitations of GWAS

Results need replication in independent samples in different populations.

A large study population is required.

GWAS studies detect association not causation.

GWASidentifies a specific location, not complete genes. Many variants identified in GWAS are nowhere near a protein-coding gene, or are within genes that were not previously believed to associated with a trait or condition.

Detects only variants that are common (>5%) in a population

Typically, for any particular trait, the cumulative effects of multiple **SNPs only explains a small fraction of an individual's risk for the trait.

Hints for use

Avoid GWAS when statistical power is limited by small sample size, such as when investigating rare traits or lethal conditions.

Larger sample sizes are required to detect an association when multiple genes are involved in a trait.

The only way to show causality is to understand the biological pathway and the role that the gene variant has in the production of the trait or disease process. Thus, the results of GWAS studies should always be considered in light of functional evidence.